Differential gene expression and co-regulated expression of genes in leukemia: an in-silico approach to identify potent biomarker
Article Main
Abstract
A biomarker can be measured, used to diagnose or classify disease, and measure progress as well as the therapeutic response of the disease. Early diagnosis and selection of appropriate treatment can be critical for the successful treatment of diseases. Identification and characterization of potent diagnostic biomarkers, and therapeutic targets rely heavily on traditional in vitro screens which require extensive resources and time. Integration of in silico screens prior to experimental validation can improve the efficiency and potency of biomarkers as well as reduce the cost and time of biomarker discovery. Considering the need, present work was undertaken to identify biomarkers for different classes of leukemia. Differential Gene Expression (DGE) analysis and co-regulated expression analysis were used for in silico identification and characterise a potent biomarker for leukemia. On the basis of in silico screening, the present study proposed seven protein-coding (CD38, TSC22D3, TNFRSF25, AGL, LARGE1, ARHGAP32, and PARM1) genes for the diagnosis of leukemia. The study also proposed a novel three-step lineage-specific model for the diagnosis of leukemia. In the three-step diagnosis model, the first group of biomarkers with an association of clinical and hematological parameters diagnose leukemia. The second group of biomarkers diagnoses acute and chronic form of leukemia. The third group of biomarkers identifies whether it belongs to myeloid lineage or lymphoid lineage.
Article Details
Article Details
Biomarkers, Co-regulated Expression, Differential Gene Expression, Leukemia
Conesa, A., Madrigal, P., & Tarazona, S. (2016). A survey of best practices for RNA-seq data analysis. Genome Biology, (17) 13.
Crushell, E., Treacy, E., Dawe, J., Durkie, M., & Beauchamp, N. (2010). Glycogen storage disease type III in the Irish population. J Inherit Metab Dis., 33, S215-S218.
Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J.Y., & Zhang, J. (2004). Bioconductor: open software development for computational biology and bioinformatics. Genome Biology, 5(10), 80.
Guin, S., Ru, Y., Agarwal, N., Lew, C.R., Owens, C., Comi, G.P., & Theodorescu, D. (2016). Loss of glycogen debranching enzyme AGL drives bladder tumor growth via induction of hyaluronic acid synthesis. Clin. Cancer Res., 1, 1274-1283.
Harder, L., Eschenburg, G., Zech, A., & Kriebitzsch, N. (2013). Aberrant ZNF423 impedes B cell differentiation and is linked to adverse outcome of ETV6-RUNX1 negative B precursor acute lymphoblastic leukemia. J Exp Med., 21, 210(11), 2289-2304.
Heberle, H., Meirelles, G.V., & Da Silva, FR. (2015). InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics, 16, 169. doi:10.1186/s12859-015-0611-3
Holroyde, C., Skutches, C., Boden, G., & Reichard, G. (1984). Glucose metabolism in cachectic patients with colorectal cancer. Cancer Res., 44, 5910-5913.
Jiang, Z., Wu, D., & Lin, S. (2016) CD34 and CD38 are prognostic biomarkers for acute B lymphoblastic leukemia. Biomark Res (4),23. doi: 10.1186/s40364-016-0080-5
Li, K., Wang, F., Cao, W.B., & Lv, X.X. (2017). TRIB3 Promotes APL Progression through Stabilization of the Oncoprotein PML-RAR? and Inhibition of p53-Mediated Senescence. Cancer Cell, 8, 31(5), 697-710.
Li, L., & Wu, P. (2020). Analysis of the expression and clinical significance of miR-382-5P in ovarian cancer based on biological information. Research square, doi: 10.21203/rs.3.rs-16753/v1.
Miller, A.L., Komak, S., Webb, M.S., Leiter, E.H., & Thompson, E.B. (2007). Gene expression profiling of leukemic cells and primary thymocytes predicts a signature for apoptotic sensitivity to glucocorticoids. Cancer Cell International, 7(1), 18. doi:10.1186/1475-2867-7-18.
Paul, A., Galler, J., Koss, M., Hagen, J., Turla, S., & Campan, M. (2008). Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer. Molecular Cancer, 7(1):62. doi: 10.1186/1476-4598-7-62.
Paulisally, L., Chizu, T., Toyomasa, K., Yusuke, N., & Koichi, M. (2014). Identification of novel epigenetically inactivated gene PAMR1 in breast carcinoma. Oncology Reports, 33(1), 267-273. doi: 10.3892/or.2014.3581.
Prada, A. J., Arroyave, J.C., & Röthlisberger, S. (2017) Molecular biomarkers in acute myeloid leukemia. Blood Rev. (1),63-76. doi: 10.1016/j.blre.2016.08.005
Rodriguez, E., & Jiang, X. (2017). Differential gene expression in disease: a comparison between high-throughput studies and the literature. BMC Medical Genomics vol, 10, 59.
Sanchez, R., & Mackenzie, S. A. (2020). Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia. Sci Rep., 10, 2123. doi:10.1038/s41598-020-58123-2.
Smyth, G.K. (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology, 3, 3.
Straus, D.S. (2013). TNF? and IL-17 cooperatively stimulate glucose metabolism and growth factor production in human colorectal cancer cells. Molecular Cancer, 12(1), 78. doi:10.1186/1476-4598-12-78
Sweet, K., Zhang, L. & Pinilla-Ibarz, J. (2013) Biomarkers for determining the prognosis in chronic myelogenous leukemia. J Hematol Oncol (6), 54. doi:10.1186/1756-8722-6-54
Tang, Y., Zheng, J., Fu, X., & Chen, Y. (2019). Bioinformatics Analysis of Differentially Expressed Genes and Their Functional Enrichment in Acute Myeloid Leukemia Bearing MLL Translocation. Biomark J., 5(2), 1.
Tripathi, S., & Pandya, H. (2016). Discovery of Novel Gene Biomarker for Acute Myeloid Leukemia Through Differential Gene Expression Analysis. Annals of Applied Bio-Sciences, 3(1), e2349-6991.
Vargova, K., Curik, N., Burda, P., & Basova, P. (2011). MYB transcriptionally regulates the miR-155 host gene in chronic lymphocytic leukemia. Blood, 117(14), 3816-3825.
Vlaanderen, J., Leenders, M., Hyam, M., Portengen, L., Kyrtopoulos, S., Berdahal I., Johanson, A., Hebels, D., Kok, T., Vineis, P., & Vermeulen, R. (2017). Exploring the nature of prediagnostic blood transcriptome markers of chronic lymphocytic leukemia by assessing their overlap with the transcriptome at the clinical stage, BMC Genomics, 18, 239.
Wei, Fu., Cheng, G., Ding, Y., Deng, Y., & Guo, P. (2020). Identification of hub genes and its correlation with the prognosis of acute myeloid leukemia based on high?throughput data analysis. Precision Radiation Oncology, 4(2), 49-56. doi: 10.1002/pro6.1089.
Yang, H., Xia, L., Chen, J., Zhang, S., Martin, V., Li, Q., Lin, S., Chen, J., Calmette, J., Lu, M., Fu, L., Lu, M., Fu, L., Yang, J., Pan, Z., Yu, K., He, J., Morand, E., Louf, G., Krzysiek, R., Zitvogel, L., Kang, B., Zhang, Z., Leader, A., Zhou, P., Lnfumev, L., Shi, M., Kroemer, G., & Ma, Y. (2019). Stress–glucocorticoid–TSC22D3 axis compromises therapy-induced antitumor immunity. Nat Med., 25(9), 1428-1441. doi: 10.1038/s41591-019-0566-4.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)
